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Artificial Intelligence Is Stuck

nytimes.com

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Re: Artificial Intelligence Is Stuck

#21

Post by nl »

phreeza wrote: post #11
unityByFreedom wrote: post #6

> An international A.I. mission focused on teaching machines to read could genuinely change the world for the better — the more so if it made A.I. a public good, rather than the property of a privileged few. > author: Gary Marcus is a professor of psychology and neural science at New York University. Not sure what he has in mind. There are already a lot of smart people building Q&A systems. We need tests to establish…

Winograd Schema and bAbI seem like good intermediate goals?

Winograd Schema yes, bAbi no. bAbi is a nice dataset for understanding how different kinds of algorithm perform, but it is trivially solvable.

Re: Artificial Intelligence Is Stuck

#23

Post by sgt101 »

adamnemecek wrote: post #15

We are using the wrong computational paradigm. We have to abandon bits and go back to analog computing in the form of analog photonic computing that gives you fast Calculus. This is painfully obvious in the case of neural networks, which run faster on an analog computer and are also easier to program.

I think you are out of date. Rectification is pretty good at removing a lot of the vanishing gradient issues that nn's used to face, and the overwhelming power of modern digital computers (50k cores is common) make this all moot as far as I can see.

Re: Artificial Intelligence Is Stuck

#24

Post by adamnemecek »

sgt101 wrote: post #23
adamnemecek wrote: post #15

We are using the wrong computational paradigm. We have to abandon bits and go back to analog computing in the form of analog photonic computing that gives you fast Calculus. This is painfully obvious in the case of neural networks, which run faster on an analog computer and are also easier to program.

I think you are out of date. Rectification is pretty good at removing a lot of the vanishing gradient issues that nn's used to face, and the overwhelming power of modern digital computers (50k cores is common) make this all moot as far as I can see.

Nope, they aren't even in the same category. E.g. Notice that there is a cpu size limit because of heat. Do you know what doesn't overheat? A photonic computer. You can build a cpu the size of a house. Also how does the number of cores constitute "overwhelming power".

Re: Artificial Intelligence Is Stuck

#25

Post by [deleted] »

rwnspace wrote: post #10

I sense the hand of an editor. Particularly regarding the title. Embodiment seems to be a branch with low-hanging fruit, when it comes to advancing AGI. I think the economic structural problems are important, but it's possible to over-egg the details and for some lab to stumble on an experimental paradigm with features we didn't realise were implicated a priori. When it comes to other AIs, the idea that we are stuck…

[deleted]

Re: Artificial Intelligence Is Stuck

#26

Post by jsemrau »

unityByFreedom wrote: post #8
jsemrau wrote: post #4

We (humanity) have made huge progress to understand images in terms of content and emotions of people. Imagenet is truly a gift to the world. However, that has brought us only a small but important step forward. Clearly expectation has to catch up to reality. However, all these solutions are becoming quickly more accessible to the laymen bringing another boost to operational efficiencies for companies worldwide.

> Clearly expectation has to catch up to reality. Woah, you feel expectations are behind reality? I feel there's a lot of news lately predicting AGI.

Well I guess it's a grammar issue ;-) Of course expectations are somewhere near HAL while we are throwing bones.

Re: Artificial Intelligence Is Stuck

#27

Post by dimatura »

adamnemecek wrote: post #15

We are using the wrong computational paradigm. We have to abandon bits and go back to analog computing in the form of analog photonic computing that gives you fast Calculus. This is painfully obvious in the case of neural networks, which run faster on an analog computer and are also easier to program.

How is an analog computer "easier to program" than a digital computer? Making neural networks do what you want is hard enough with the help of tons of libraries, decent scripting languages, the ability to dump the weights into a file and inspect them, etc. Programming with an analog computer, which I'm guessing would be something like programming with FPGAs, sounds like a nightmare in comparison.

Re: Artificial Intelligence Is Stuck

#28

Post by peterburkimsher »

The article is calling us to teach AI to read words and phrases and look for the meaning, not just a statistical correlation.

I put dictionary data into Pingtype English to try to parse phrases instead of just words. e.g. "pick [something] up". The purpose is to do word-for-word translation to Chinese as an educational tool. It's not perfect, but the dictionary is editable. You can contact me if you want to discuss new ways of extending the features (e.g. data from UrbanDictionary, movie subtitles, etc).

http://pingtype.github.io/english.html

I also want to correct the author that CERN does not have billions of dollars of funding. There's only about 5000 staff, and the other 10,000 people working there are funded by universities elsewhere who send them to CERN to do the research.

Re: Artificial Intelligence Is Stuck

#29

Post by adamnemecek »

dimatura wrote: post #27
adamnemecek wrote: post #15

We are using the wrong computational paradigm. We have to abandon bits and go back to analog computing in the form of analog photonic computing that gives you fast Calculus. This is painfully obvious in the case of neural networks, which run faster on an analog computer and are also easier to program.

How is an analog computer "easier to program" than a digital computer? Making neural networks do what you want is hard enough with the help of tons of libraries, decent scripting languages, the ability to dump the weights into a file and inspect them, etc. Programming with an analog computer, which I'm guessing would be something like programming with FPGAs, sounds like a nightmare in comparison.

Because neural networks are fundamentally dynamic systems that are much easier to model with continuous signals than discrete bits. A lot of the hardness comes from the fact that you are discretizing fundamentally continuous signals.

Re: Artificial Intelligence Is Stuck

#30

Post by scotty79 »

nl wrote: post #19
asketak wrote: post #9

Most of the AI progress in last years is just tuning pattern recognition algorithms. We can not expect these algorithms to produce results like humans, because humans have a lot of information not just from percieving the world, but their patterns of thinking are also vastly dependend on the underlying structure of brain, that has developed over milions of years of evolution. If there is a cliff, toddlers are scared…

If there is a cliff, toddlers are scared of being nearby. Not sure if you are a parent, but this isn't the case at all!

https://youtu.be/WanGt1G6ScA

Not sure what to think about it.

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